Article Text

Prevalence, Medicaid use and mortality risk of low FEV1 in adults aged 20–35 years old in the USA: evidence from a population-based retrospective cohort study
  1. Zihui Wang1,2,
  2. Yun Li1,2,
  3. Lunfang Tan1,2,
  4. Shuyi Liu1,2,
  5. Zhufeng Wang1,2,
  6. Qing Zhang1,2,
  7. Junfeng Lin1,2,
  8. Jinhai Huang1,2,
  9. Lina Liang1,2,
  10. Yi Gao1,2,
  11. Nanshan Zhong1,2 and
  12. Jinping Zheng1,2
  1. 1Guangzhou Institute of Respiratory Disease, Guangzhou, Guangdong, China
  2. 2First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China
  1. Correspondence to Dr Jinping Zheng; jpzhenggy{at}163.com

Abstract

Background The prevalence, Medicaid use and mortality risk associated with low forced expiratory volume in 1 s (FEV1) among young adults aged 20–35 years are not well understood, despite its potential implications for the development of chronic pulmonary disease and overall prognosis.

Methods A retrospective cohort study was conducted among young adults aged 20–35 years old, using data from the National Health and Nutrition Examination Survey, National Death Index and Centers for Medicare & Medicaid Services. Participants were categorised into a low FEV1 group (pre-bronchodilator FEV1%pred <80%) and a normal FEV1 group (FEV1%pred ≥80%). Weighted logistic regression analysis was employed to identify the risk factors associated with low FEV1, while Cox proportional hazard models were used to calculate the hazard ratio (HR) for Medicaid use and the all-cause mortality between the two groups.

Results A total of 5346 participants aged 20–35 were included in the study, with 329 in the low FEV1 group and 5017 in the normal group. The weighted prevalence of low FEV1 among young adults was 7.1% (95% CI 6.0 to 8.2). Low body mass index (OR=3.06, 95% CI 1.79 to 5.24), doctor-diagnosed asthma (OR=2.25, 1.28 to 3.93), and wheezing or whistling (OR=1.57, 1.06 to 2.33) were identified as independent risk factors for low FEV1. Over a 15-year follow-up, individuals in the low FEV1 group exhibited a higher likelihood of Medicaid use compared with those in the normal group (HR=1.73, 1.07 to 2.79). However, there was no statistically significant increase in the risk of all-cause mortality over a 30-year follow-up period (HR=1.48, 1.00 to 2.19).

Conclusions A considerable portion of young adults demonstrated low FEV1 levels, a characteristic that was associated with a higher risk of Medicaid use over a long-term follow-up, yet not linked to an augmented risk of all-cause mortality.

  • Clinical Epidemiology
  • Respiratory Function Test

Data availability statement

Data sharing is not applicable because no new dataset was generated and the data used in this study were originally from publicly available databases.

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This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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WHAT IS ALREADY KNOWN ON THIS TOPIC

  • Previous studies have suggested that a low forced expiratory volume in 1 s (FEV1) may be linked to a poorer prognosis, yet there is a lack of robust evidence supporting this relationship.

WHAT THIS STUDY ADDS

  • This study found that 7.1% of young adults had a low FEV1. Risk factors for low FEV1 included low body mass index, doctor-diagnosed asthma, and wheezing or whistling. Individuals with low FEV1 were more likely to receive Medicaid services, but did not have a statistically significant increased risk of all-cause mortality.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

  • Further research is warranted to investigate the significance of early detection and intervention for low FEV1 in adults to enhance long-term outcomes.

Introduction

Chronic obstructive pulmonary disease (COPD) is a common respiratory disease characterised by persistent airflow limitation and chronic respiratory symptoms. According to the latest findings from the Global Burden of Disease (GBD) study, there were 212 million COPD patients worldwide, 3.3 million deaths directly related to COPD in 2019, 74.4 million disability-adjusted life-year per year, making COPD an important disease burden that raised a threat to human health globally.1 COPD was often viewed as a consequence of progressive deterioration in lung function over time, as assessed by the forced expiratory volume in 1 s (FEV1).2 However, emerging evidence has suggested that a substantial proportion of individuals with COPD exhibit suboptimal peak lung function in early adulthood and experience a slow decline in FEV1.3 Specifically, individuals with an FEV1 of the predicted value (FEV1%pred) below 80% prior to the age of 40 were found to have a significantly higher likelihood of developing COPD after a 20-year follow-up period.3 These findings underscore the heightened risk of COPD development among individuals with diminished FEV1 levels.

Recently, there has been a growing focus on the association between low FEV1 and COPD, as well as its implications for future health outcomes. Research conducted across three cohorts has demonstrated that individuals with low FEV1 exhibit a higher prevalence of comorbidities in early adulthood and an elevated risk of all-cause mortality.4 The Preserved Ratio Impaired Spirometry (PRISm), previously referred to as non-specific pulmonary dysfunction,5 is characterised by a ratio of FEV1 to forced vital capacity (FVC) ≥0.7 and an FEV1%pred <80%. It has been previously reported that individuals with PRISm are at an increased risk of developing COPD and experiencing higher rates of cardiovascular and respiratory-related mortality.6–9 Given the significant impact of low FEV1 and PRISm on public health, it is plausible to assume that low FEV1 in adults aged 20–35 could lead to a poor prognosis.

The natural trajectory of lung function throughout the lifespan can be delineated into three phases: the development phase extending from birth to early adulthood, the prolonged plateau phase and the subsequent physiological decline associated with ageing.10 Since lung growth and development are influenced by multifactorial influences,11 individuals may exhibit varying trajectories of lung function. Consequently, it is imperative to evaluate the implications of declining lung function across different phases, particularly during the developmental phase, to effectively prevent and manage chronic respiratory disorders. As lung function typically peaks between the ages of 20 and 2512 and remains relatively stable from ages 20 to 35,13 there is a need to investigate the prevalence of low FEV1 and its associated characteristics and prognosis during this precise stage. Nevertheless, existing research has predominantly focused on populations aged 40 and above, including individuals with PRISm.

In order to address the aforementioned concerns and enhance comprehension of diminished lung function among young individuals, we conducted a retrospective cohort study using data from the National Health and Nutrition Examination Survey (NHANES) in the USA. Following the definition proposed by Lange et al,3 individuals aged 20–35 with an FEV1%pred below 80% were classified as ‘low FEV1 in young adults’ in this investigation. The primary objective of this study was to investigate the demographic characteristics, prevalence, risk factors, Medicaid use and mortality rates associated with low FEV1 in young adults.

Methods

Study population

The NHANES used a complicated, multi-stage probability sampling methodology. Data collection was derived from personal interviews and mobile physical examinations. The study design and data collection procedures have been previously described.14

Our cohort consisted of individuals aged 20–35 years who completed the baseline survey and pulmonary function test during NHANES III (1988–1994), and were subsequently monitored for all-cause mortality and Medicaid utilisation.

This cohort study used exclusively publicly available and did not necessitate approval from institutional review boards. This study adhered to the Strengthening the Reporting of Observational Studies in Epidemiology reporting guideline.

Assessments of baseline characteristics, Medicaid use and mortality of the low FEV1 in young adults

Low lung function in young adults, defined as pre-bronchodilator FEV1%pred <80% in individuals aged 20–35 years, was categorised as low FEV1.3 12 13 Personal FEV1 predicted values were determined using the race-specific Global Lung Function Initiative equations for spirometry.15 The race-agnostic equations were applied when the race information was missing.

Baseline interviews and physical examinations yielded data on covariates, such as sex, age, race, body mass index (BMI), family income, smoking status, passive smoking, self-reported chronic diseases, prescribed medication and respiratory symptoms. A BMI below 18.5 was classified as low, while a BMI above 25 was classified as high. Mortality status records (time from baseline to all-cause mortality) and Medicaid records (time from baseline to first use) were determined through the linked files from the National Death Index (NDI) and Centers for Medicare & Medicaid Services (CMS) using a unique participant identifier. The NDI provided longitudinal data from the baseline date through 31 December 2019, while the CMS provided data from the baseline date through 31 December 2014. Medicaid, a government-funded programme with supplementary support from recipient premiums, provides healthcare coverage for eligible individuals. Medicare eligibility is extended to those eligible for Social Security benefits. The use of Medicaid is determined by analysing enrolment and claims data for inpatient hospital, long-term care and other healthcare services. The baseline for survival analysis was defined as the time of spirometry testing in the NHANES. Months from baseline to the date of the adverse events, loss to follow-up or last follow-up data were calculated for the Medicaid use and mortality.

No data gaps were filled in the raw database.

Statistical analysis

Given the intricate survey design of NHANES (wwwn.cdc.gov/nchs/nhanes/tutorials), Weights and Taylor Linearization procedures were used to generate estimates (eg, prevalence and variance estimation) that are reflective of the US population.

Demographic characteristics of individuals aged 20–35 were presented as mean (SD) for continuous measures and number (percentage) for categorical measures. A t-test was used to analyse continuous variables, while a χ2 test was employed to assess categorical variables in order to identify differences between the low FEV1 and normal FEV1 groups. Frequency distribution graphs were used to display spirometry measures (ie, FEV1, FVC, FEV1/FVC, FEV3, FEV6, MMEF[Maximum Midexpiratory Flow] and FEF75) in the low FEV1 and normal FEV1 groups. The prevalence of low FEV1 among young individuals was stratified by sex, race, self-reported asthma and BMI and compared using a χ2 test.

Logistic regression with weights was used to ascertain potential risk factors for low FEV1. The study used OR and 95% CI to present the association between risk factors and low FEV1. Cox proportional hazard models with weight design were employed to estimate the HR and 95% CIs for the relationship between Medicaid use and the low FEV1 group in comparison to the normal FEV1 group, adjusting for potential confounders such as sex, age, BMI and race. Additionally, the HR and 95% CIs for the relationship between all-cause mortality and the low FEV1 group versus the normal FEV1 group were determined using the aforementioned methodology.

Statistical significance was determined at a two-sided p value of less than 0.05. All analyses were performed using the R software (V.4.1) with the ‘tableone’, ‘survey’, ‘ggplot2’ and ‘survival’ packages.

Patient and public involvement

There was no specific patient or public involvement in the planning or execution of the study.

Results

Demographic and clinical characteristics of the low FEV1 group

A total of 5802 participants aged 20–35 completed the baseline survey from NHANES III (1988–1994). After excluding those who did not complete a pulmonary function test (n=456), 5346 young participants were ultimately enrolled in our cohort for analysis, with 329 in the low FEV1 group and 5017 in the normal group. As shown in table 1, significant differences were observed in certain unweighted variables between the two groups. In comparison to the normal group, the low FEV1 group exhibited higher percentages of individuals with low BMI (7.0% vs 2.4%), African American ethnicity (40.7% vs 31.9%), current smoking habits (32.8% vs 30.7%), pack-years of smoking (2.0±5.0 vs 1.3±3.6) and comorbidities such as doctor diagnosis hypertension (14.2% vs 10.3%), asthma (17.3% vs 5.5%) and chronic bronchitis (6.1% vs 3.2%). Additionally, the low FEV1 group had higher rates of clinical symptoms such as shortness of breath (23.7% vs 15.0%), wheezing or whistling (24.3% vs 12.1%). In terms of pulmonary function, the low FEV1 group exhibited lower mean values compared with the normal group for pre-FEV1 (2519.0±643.7 mL vs 3599.9±753.8 mL), pre-FVC (3310.4±973.3 mL vs 4294.5±962.1 mL) and pre-FEV1/FVC (78.0±12.0% vs 84.0±6.0%).

Table 1

Demographic and clinical characteristics of the low FEV1 and normal group in the USA 1988–1994

Prevalence of low FEV1 in young adults aged 20–35 years

The weighted prevalence of low FEV1 in adults aged 20–35 years was 7.1% (95% CI 6.0 to 8.2), with 6.8% (95% CI 5.4 to 8.2) for females and 7.5% (95% CI 5.8 to 9.2) for males. As shown in figure 1, participants with low BMI exhibited a higher prevalence of low FEV1 compared with those with high or normal BMI, with rates of 17.8% (95% CI 8.9 to 26.7), 7.6% (95% CI 5.9 to 9.4) and 6.1% (95% CI 4.7 to 7.5), respectively. Participants with other/mixed race had a higher prevalence of low FEV1 compared with those with African American or white race, with rates of 9.9% (95% CI 6.53 to 13.22), 7.5% (95% CI 6.15 to 8.84) and 6.43% (95% CI 5.11 to 7.75), respectively. Participants with a smoking index of ≥10 pack-years were more likely to have low FEV1 compared with those with a smoking index of <10 pack-years, with rates of 12.2% (95% CI 6.8 to 17.6) and 7.4% (95% CI 6.1 to 8.7), respectively. Additionally, participants with doctor-diagnosed asthma or chronic bronchitis had a higher prevalence of low FEV1 compared with those without asthma or chronic bronchitis, and participants with self-reported shortness of breath and wheezing or whistling were more likely to have low FEV1.

Figure 1

Prevalence of low FEV1 in young adults in the USA 1988–1994. Prevalence was estimated using sample weights from National Health and Nutrition Examination Survey. The Rao-Scott χ2 test was applied to compare the prevalence among subgroups. AfrAm refers to African Americans, white indicates the white race and other/mixed pertains to individuals of other or mixed races. FEV1, forced expiratory volume in 1 s.

Risk factors for low FEV1 in young adults aged 20–35 years

Univariate logistic analysis showed that low BMI (OR=3.34, 95% CI 2.09 to 5.36), pack-years ≥10 (OR=1.73, 95% CI 1.07 to 2.80), diagnosed with asthma (OR=2.76, 95% CI 1.71 to 4.44), diagnosed with chronic bronchitis (OR=2.12, 95% CI 1.06 to 4.23), shortness of breath (OR=1.91, 95% CI 1.29 to 2.82) and wheezing or whistling (OR=2.19, 95% CI 1.53 to 3.13) were risk factors for low FEV1 (figure 2A).

Figure 2

Logistic analysis of risk factors for low FEV1 in young adults. (A) The results of univariable logistic analysis. (B) The results of multivariable logistic analysis. OR and 95% CI were estimated using sample weights from National Health and Nutrition Examination Survey. BMI<18.5 was regarded as BMI low. BMI>25 was regarded as BMI high. BMI, body mass index; FEV1, forced expiratory volume in 1 s.

Multivariate logistic regression analysis (figure 2B) showed that low BMI (OR=3.06, 95% CI 1.79 to 5.24), doctor-diagnosed asthma (OR=2.25, 95% CI 1.28 to 3.93), and wheezing or whistling (OR=1.57, 95% CI 1.06 to 2.33) were independent risk factors for low FEV1.

Prognosis of low FEV1 in young adults aged 20–35 years

Kaplan-Meier curves showed the low FEV1 group had a higher risk of receiving Medicaid use than the normal group over a 15-year follow-up (p<0.001, figure 3). Results of the Cox model indicated that low FEV1 was a risk factor for the use of Medicaid (HR=1.73, 95% CI 1.07 to 2.79, p=0.02, unadjusted; HR=1.61, 95% CI 1.05 to 2.45, p=0.03, adjusted for age, sex, race and BMI, online supplemental eTable 1). However, over a 30-year follow-up, no significant difference was observed in the risk of all-cause mortality between the low FEV1 group and the normal group (figure 4). Results of the Cox model also showed no statistically significant increased risk for low FEV1 (HR=1.48, 95% CI 1.00 to 2.19, p=0.05, unadjusted; HR=1.42, 95% CI 0.94 to 2.13, p=0.10, adjusted for age, sex, race and BMI, online supplemental eTable 2).

Figure 3

Kaplan-Maier survival curves of Medicaid use for the low FEV1 and normal group. Kaplan-Maier survival curves were performed to estimate the risk of Medicaid use. HR and 95% CI were estimated using sample weights from National Health and Nutrition Examination Survey. FEV1, forced expiratory volume in 1 s.

Figure 4

Kaplan-Maier survival curves for mortality in the low FEV1 and normal group. Kaplan-Maier survival curves were performed to estimate the risk of Medicaid use. HR and 95% CI were estimated using sample weights from National Health and Nutrition Examination Survey. FEV1, forced expiratory volume in 1 s.

Risk factors for mortality in low FEV1 population

Based on the presence of death during a 30-year follow-up period, the low FEV1 cohort was stratified into the deceased group (n=39) and the survival group (n=290). Univariate logistic analysis showed that BMI (OR=1.07, 95% CI 1.02 to 1.13) and pack-years ≥10 (OR=2.28, 95% CI 1.13 to 4.62) were risk factors for low FEV1, whereas other/mixed race (OR=0.05, 95% CI 0.01 to 0.22) was a protective factor (figure 5A).

Figure 5

Logistic analysis of risk factors for mortality in the low FEV1 population. (A) The results of univariable logistic analysis. (B) The results of multivariable logistic analysis. OR and 95% CI were estimated using sample weights from National Health and Nutrition Examination Survey. BMI<18.5 was defined as BMI low. BMI>25 was defined as BMI high. BMI, body mass index; FEV1, forced expiratory volume in 1 s.

Multivariate logistic regression revealed that age (OR=1.05, 95% CI 1.01 to 1.10), BMI (OR=1.05, 95% CI 1.02 to 1.08) and pack-years ≥10 (OR=2.10, 95% CI 1.36 to 3.24) were independent risk factors for mortality in the low FEV1 population, while other/mixed race (OR=0.59, 95% CI 0.38 to 0.93) and white race (OR=0.59, 95% CI 0.41 to 0.83) were protective factors (figure 5B).

Discussion

Our research, using data from the NHANES III, established a population-based retrospective cohort, discovered that the prevalence of low FEV1 in the young population was 7.1% in the USA. Independent risk factors for low FEV1 included low BMI, doctor-diagnosed asthma, and symptoms of wheezing or whistling. Individuals with low FEV1 were more likely to use Medicaid services compared with those with normal FEV1 levels, yet both groups exhibited a similar risk of all-cause mortality. Furthermore, a pack-year history of 10 or more was an independent risk factor for mortality within the low FEV1 population.

To the best of our knowledge, this study marked the first report on the prevalence of low FEV1 among individuals aged 20–35 years old. We found that the prevalence of low FEV1 in such adults in America was 7.1%, without a significant difference between men and women. As reported previously, the prevalence of low FEV1 among adults aged 25–40 in three cohorts ranged from 4% to 13%.4 The Burden of Lung Disease study showed that the incidence of PRISm (low FEV1 without airflow limitation) in people over 40 years old was 14.2%.6 The British Biobank study showed that the incidence of PRISm among people aged 40–69 was 11%.16 Additionaly, results from the COPDGene cohort showed that the prevalence of PRISm in adults aged 45–80 was around 10.4%–12.5%.7 17 18 All these studies suggested a positive correlation between advancing age and the prevalence of low FEV1, which may be attributed to longer exposure to smoking, air pollution and lung infection among the older population.

Our observations revealed that low BMI was an independent risk factor for low FEV1. This was possible because adults with low BMI suffer from nutrient deprivation and pulmonary hypoplasia, which result in reduced FEV1 and FVC. Prior studies have also noted strong links between low BMI and conditions such as PRISm, COPD and other lung function impairments.8 19–22 However, contrasting evidence from another study indicated that individuals aged 25–40 with low FEV1 did not necessarily present lower BMI compared with those with normal FEV1 levels.4 These variations in findings might be attributed to diverse racial composition and age distribution in different studies. Additionally, doctor-diagnosed asthma emerged as an independent risk factor for low FEV1. Patients with asthma are prone to impaired FEV1, suggesting that effective asthma management could mitigate the risk of low FEV1 among young adults. In addition, participants with wheezing or whistling, often associated with asthma or other respiratory conditions, were more likely to have low FEV1. Notably, we should also pay attention to the other risk factors which were not included in our study. As reported previously, premature birth, low birth weight and adolescent smoking were associated with impaired lung growth and development, and low lung function in infancy can persist into adulthood,23 24 causing failure to develop to normal peak levels in FEV1.25 26 Furthermore, air pollution was demonstrated to have an adverse impact on children’s lung development from 10 years old to 18 years old, and reducing pollution levels could help improve children’s respiratory health.27 28 Despite the plasticity of individual lung function during childhood, the lung function catch-up rate is relatively low (14.5%).29 Therefore, it is crucial to address risk factors for low FEV1 in the prenatal period, childhood and early adulthood, including optimising growth and nutrition, preventing early infections and reducing air pollution.

The present study showed that the low FEV1 population was more likely to receive medical assistance over 15 years. Lung impairment is caused by various genetic or environmental factors, which might also compromise the development of other organ systems.4 We speculated that multi-system health problems could increase the overall medical burden. However, we found that compared with the normal group, the low FEV1 group did not have a higher risk of all-cause death during 30 years of follow-up (HR=1.48, p=0.05). This was contrary to the previous studies in which low lung function in early adulthood was associated with premature death,4 participants with PRISm had higher mortality than those with normal lung function,7 and low levels of FEV1 achieved by the age of 21–35 years could predict the risk of early cardiopulmonary mortality.30 The discrepancy with previous studies may be attributed to the following factors. First, the lack of association between low FEV1 and all-cause mortality in our study was probably a matter of statistical power since the number of deaths in this cohort was relatively small (n=39). Second, most chronic respiratory diseases progress slowly, and the adverse effects of low FEV1 were not fatal even after 30 years of follow-up, so an extended follow-up period is recommended to address the impact of low FEV1 on mortality in future research. In addition, causes of death other than the cardiopulmonary system, such as unintentional injury and malignant diseases, may account for a substantial proportion of deaths in the young population.

Our study showed that pack-years ≥10 was an independent risk factor for all-cause mortality in the low FEV1 population, suggesting that heavy smokers in the low FEV1 population may have worse outcomes. Smoking is a risk factor not only for lung cancer and other respiratory diseases but also for cardiovascular and cerebrovascular diseases. The GBD study showed that tobacco use was the second risk factor globally for attributable deaths and accounted for 8.71 million deaths in 2019.31 Therefore, smoking cessation is encouraged in the low FEV1 population and may help improve their prognosis.

The significant contributions of our study include conducting a population-based retrospective cohort analysis and being the first to examine the prevalence, characteristics, risk factors, Medicaid usage and mortality associated with low FEV1 within a specific US population. However, this study has some limitations. First, we could not evaluate longitudinal changes in lung function in the low FEV1 population, as well as the relationship between such population and COPD warrants further study. Second, due to the absence of detailed data on key background factors such as birth history, family history of respiratory diseases and history of childhood respiratory diseases, we could not assess the effect of the above risk factors for low FEV1. Third, owing to ethical constraints, detailed information about Medicaid assistance was not available, and whether the respiratory diseases account for the main aid remained unclear.

Conclusion

A notable portion of young adults exhibited low FEV1 levels, with independent risk factors including low BMI, doctor-diagnosed asthma, and wheezing or whistling. A significant relationship was observed between low FEV1 and increased Medicaid usage, while no direct correlation was found between low FEV1 and all-cause mortality. Individuals with a history of smoking and diminished FEV1 levels warrant attention, with smoking cessation being strongly recommended to mitigate premature mortality. Nevertheless, further research is necessary to confirm these findings.

Data availability statement

Data sharing is not applicable because no new dataset was generated and the data used in this study were originally from publicly available databases.

Ethics statements

Patient consent for publication

References

Supplementary materials

  • Supplementary Data

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Footnotes

  • ZW, YL, LT and SL contributed equally.

  • Contributors ZW is responsible for the overall content as the guarantor. ZW, YL, LT and SL had full access to all of the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis. ZW, YL, LT and SL contributed equally as cofirst authors. Concept and design: ZW, YG, NZ and JZ. Acquisition, analysis or interpretation of data: ZW, YL, LT, SL. Drafting of the manuscript: all authors. Critical revision of the manuscript for important intellectual content: all authors. Statistical analysis: ZW, YL, LT. Obtained funding: YG, NZ and JZ. Administrative, technical or material support: ZW, YL, LT, NZ and JZ. Supervision: YG, NZ and JZ.

  • Funding This study was sponsored by Zhongnanshan Meical Foundation of Guangdong Province (ZNSA-2021011),R&D Programme of Guangzhou National Laboratory, Grant [No. SRPG22-018], the Medical Scientific Research Foundation of Guangdong Province, China [C2021073] and the National Key Technology R&D Programme [2018YFC1311901 and 2016YFC1304603]. However, study funding had no influence on the study.

  • Competing interests None declared.

  • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.